A visualization of 3D proteome universe: mapping of a proteome ensemble into 3D space based on the protein-structure composition

Ordered genome change of plant and animal body cells revealed by the genome profiling method

In the past, it was widely thought that, although epigenetically different, the genome sequences of cells are basically the same in a single body. In retrospect, the genome-uniformity idea may have been naïve, considering that DNA polymerases cannot be perfect. Here, a systemic, not sporadic, genome change was demonstrated in a single plant (Arabidopsis) and animal (zebrafish) body using genome DNAs taken in an ordered manner using the genome profiling method. This can be explained because mutations accumulate additively in progeny cells, and these results are critically significant for developmental and oncological research.

Big data and clinicians: a review on the state of the science

BACKGROUND

In the past few decades, medically related data collection saw a huge increase, referred to as big data. These huge datasets bring challenges in storage, processing, and analysis. In clinical medicine, big data is expected to play an important role in identifying causality of patient symptoms, in predicting hazards of disease incidence or reoccurrence, and in improving primary-care quality.

OBJECTIVE

The objective of this review was to provide an overview of the features of clinical big data, describe a few commonly employed computational algorithms, statistical methods, and software toolkits for data manipulation and analysis, and discuss the challenges and limitations in this realm.

METHODS

We conducted a literature review to identify studies on big data in medicine, especially clinical medicine. We used different combinations of keywords to search PubMed, Science Direct, Web of Knowledge, and Google Scholar for literature of interest from the past 10 years.

RESULTS

This paper reviewed studies that analyzed clinical big data and discussed issues related to storage and analysis of this type of data.

CONCLUSIONS

Big data is becoming a common feature of biological and clinical studies. Researchers who use clinical big data face multiple challenges, and the data itself has limitations. It is imperative that methodologies for data analysis keep pace with our ability to collect and store data.